Analytical Tools for Complex Brain Networks: Fusing Novel Statistical Methods and Network Science to Understand Brain Function

脑网络分析的新兴领域认为大脑是一个系统，提供了深刻的临床 洞察系统级属性和健康结果之间的联系。网络科学促进了这些 分析和我们对大脑结构和功能组织方式的理解。尽管如此， 对网络群进行统计建模和比较的方法已经落后。的发展。 这种方法将构成一项重大创新，并对科学进步产生重大影响 研究人员试图更好地了解大脑功能以及它如何在不同的精神状态和 疾病状况。当前的分组比较方法依赖于(A)特定提取的摘要 指标，由于敏感性/特异性低和缺乏临床可解释性，其临床价值有限，或者 (B)忽略网络固有拓扑属性的基于质量的单变量结点或基于边的对比 并产生很差的统计能力。虽然一些单变量方法已被证明是有用的，但更深入地收集 要洞察职能组织的变化，需要利用来自整个大脑的数据的方法 网络。我们目前没有能力回答许多根本和紧迫的问题，包括 认知与(A)休息-任务脑网络变化，(B)任务内动态脑功能的关系 网络变化，以及(C)脑网络拓扑。在本提案中，我们将通过融合以下内容来满足这些需求 新的统计方法和基于网络的功能神经图像分析来提高我们的 了解正常和异常的大脑功能。更具体地说，我们将：开发混合的 允许集成多任务脑网络数据以评估状态变化(AIM)的建模框架 1a)，并允许评估任务内网络动态(目标1b)，开发置换测试 用于脑网络比较的框架，允许评估连续预测和控制 用于混淆协变量(目标2)，并开发和部署实现新的 方法(目标3)。我们的新方法具有变革的潜力：它们将允许使用经过验证的统计学 比较脑网络从而阐明异常脑的神经生物学相关性的方法 改变。这项创新将使研究人员能够研究表型特征如何与大脑相关 网络组织，对这一领域的进一步进展至关重要。从这个项目中获得的洞察力将 对许多大脑疾病和慢性健康状况的研究是重要的；它们也将具有临床意义 在精准医学战略领域的效用。

The emerging area of brain network analysis considers the brain as a system, providing profound clinical insight into links between system-level properties and health outcomes. Network science has facilitated these analyses and our understanding of how the brain is structurally and functionally organized. Nonetheless, methods for statistically modeling and comparing groups of networks have lagged behind. The development of such methods would constitute a significant innovation and have a significant impact on scientific progress for researchers seeking to better understand brain function and how it changes across different mental states and disease conditions. Current approaches for group comparisons rely on either (a) a specific extracted summary metric, which has limited clinical value due to low sensitivity/specificity and a lack of clinical interpretability, or (b) mass-univariate nodal or edge-based contrasts that ignore the network’s inherent topological properties and yield poor statistical power. While some univariate approaches have proven useful, gleaning deeper insights into changes in functional organization demands methods that leverage the data from an entire brain network. We are currently ill-equipped to answer many fundamental and pressing questions, including the relationship between cognition and (a) rest-to-task brain network changes, (b) within-task dynamic brain network changes, and (c) brain network topology. In this proposal, we will address these needs by fusing novel statistical methods with network-based functional neuroimage analysis to advance our understanding of normal and abnormal brain function. More specifically we will: Develop a mixed modeling framework that allows integrating multitask brain network data to assess state changes (Aim 1a) and that allows assessing within-task network dynamics (Aim 1b), develop a permutation testing framework for brain network comparisons that allows assessing continuous predictors and controlling for confounding covariates (Aim 2), and develop and deploy a Matlab package implementing the new methods (Aim 3). Our novel methods have transformative potential: they will allow use of validated statistical methods to compare brain networks and thereby illuminate neurobiological correlates of abnormal brain changes. This innovation will enable researchers to investigate how phenotypic traits are related to brain network organization, and are critical for further progress in this field. The insights gained from this project will be important for the study of numerous brain diseases and chronic health conditions; they will also have clinical utility in the realm of precision medicine strategies.